Raul Ramirez Velarde du 24/01/09 AU 04/02/09 – Professeur de l’Université de Monterey, Mexique
Abstract :
We will present a model that uses large-deviations and the Pareto probability distribution to model self-similar data in high-performance infrastructure, such as the one found on computational and data grids, transactional and computational clusters, and multimedia streaming.
We will also show how Principal Component Analysis can reduce dimensionality of data, such as the one produced by different types of problems, user preferences and behaviour, and resource popularity.
